Published in Conference on Robot Learning (CoRL), 2025
The paper introduces an object-relative control framework for visual navigation using a relative 3D scene graph, enabling more flexible, embodiment-invariant path planning and control that generalizes better across tasks and real-world environments than traditional image-relative methods.
Published in International Conference on Learning Representations (ICLR), 2025
This paper proposes a new framework for evaluating LLM-based forecasters by measuring their logical consistency, introducing an “arbitrage metric” that correlates strongly with future forecasting accuracy, even when the ground truth is unknown.
Published in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2025
This paper presents SparseLoc, a global localization system that uses vision-language models to build sparse, semantic maps. It achieves localization accuracy comparable to dense LiDAR methods while using 1/500th of the points.